A random forest and simulation approach for scheduling operation rooms: Elective surgery cancelation in a Chinese hospital urology department

What is it about?

Many hospitals encounter surgery cancelations for various reasons. We present a methodology applying data mining and simulation to optimize operating room (OR) scheduling in a urology department in West China Hospital. To the best of our knowledge, this is 1 of the first efforts to seek an optimal schedule solution based on cancelation risk of elective surgeries as well as OR allocation between elective and nonelective surgeries. First, chi-square test and random forest prediction modeling were used to predict potential elective surgeries with high cancelation risk, and the factors, including surgeon, number of days since admission of patient, first surgery or not, etc., that influence elective surgery cancelation were identified. Second, a simulation technology was designed to compare 7 different scheduling strategies. The results demonstrated that for elective surgery, cancelation rate low surgery first outperformed the others and increased the productivity of the ORs from 72% to 83%, while for nonelective surgery performed in a separate OR, there was no improvement because the supply was greater than necessary at present. However, in total, the selected strategies led to 7% higher productivity.

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Photo by JAFAR AHMED on Unsplash

Photo by JAFAR AHMED on Unsplash

Why is it important?

Operating room is a hospital's revenue and cost center and is closely linked with other key hospital resources. The operating performance of the OR is crucial for effective and efficient hospital management. However, at present, ORs in most hospitals at home and abroad are plagued by elective surgery cancelation, which negatively influences the hospitals, medical staff, patients, and their families. To address the problem of elective surgery cancelation and to improve the operating performance of the ORs in the urology department in WCH, this study build RF classification models to identify potential elective surgeries with high cancelation risk. Discrete event simulation techniques were used to perform and compare different surgical scheduling strategies.

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